code: def sparse_data_transformed():
      tests_dir = __file__
      os.chdir(os.path.dirname(tests_dir))
  
      decoder = arff.ArffDecoder()
      with open(os.path.join("datasets", "dataset.arff")) as fh:
          dataset = decoder.decode(fh, encode_nominal=True)
  
      # -1 because the last attribute is the class
      attribute_types = [
          "numeric" if type(type_) != list else "nominal"
          for name, type_ in dataset["attributes"][:-1]
      ]
      feat_type = {
          i: "categorical" if attribute == "nominal" else "numerical"
          for i, attribute in enumerate(attribute_types)
      }
  
      data = np.array(dataset["data"], dtype=np.float64)
      X = data[:, :-1]
      y = data[:, -1].reshape((-1,))
  
      # First, swap NaNs and zeros, because when converting an encoded
      # dense matrix to sparse, the values which are encoded to zero are lost
      X_sparse = X.copy()
      NaNs = ~np.isfinite(X_sparse)
      X_sparse[NaNs] = 0
      X_sparse = sparse.csr_matrix(X_sparse)
  
      ohe = FeatTypeSplit(feat_type=feat_type)
      X_transformed = X_sparse.copy()
      X_transformed = ohe.fit_transform(X_transformed)
      imp = SimpleImputer(copy=False)
      X_transformed = imp.fit_transform(X_transformed)
      standard_scaler = StandardScaler(with_mean=False)
      X_transformed = standard_scaler.fit_transform(X_transformed)
  
      # Transform the array which indicates the numerical metafeatures
      feat_type_transformed = {
          i: "numerical" for i in range(X_transformed.shape[1])
      }
  
      X = X_sparse
      X_transformed = X_transformed
      y = y
      mf = meta_features.metafeatures
      helpers = meta_features.helper_functions
      logger = logging.getLogger()
  
      # Precompute some helper functions
      helpers.set_value(
          "PCA",
          helpers["PCA"](X_transformed, y, logger),
      )
      helpers.set_value(
          "MissingValues",
          helpers["MissingValues"](X, y, logger, feat_type),
      )
      mf.set_value(
          "NumberOfMissingValues",
          mf["NumberOfMissingValues"](X, y, logger, feat_type),
      )
      helpers.set_value(
          "NumSymbols",
          helpers["NumSymbols"](X, y, logger, feat_type),
      )
      helpers.set_value(
          "ClassOccurences",
          helpers["ClassOccurences"](X, y, logger),
      )
      helpers.set_value(
          "Skewnesses",
          helpers["Skewnesses"](
              X_transformed, y, logger, feat_type_transformed
          ),
      )
      helpers.set_value(
          "Kurtosisses",
          helpers["Kurtosisses"](
              X_transformed, y, logger, feat_type_transformed
          ),
      )
      return X_transformed, y, feat_type_transformed
  
imports: import logging
  import os
  import arff
  import numpy
  from scipy import sparse
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  import autosklearn.metalearning.metafeatures.metafeatures
  from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: sparse_data_transformed() and returns: X_transformed,  y,  feat_type_transformed